Given a demo relation (a pair of word forms) and a query word, we devise a character-based recurrent neural network architecture using three separate encoders and a decoder, trained to predict the missing second form of the query word. Our results show that the exact form can be predicted for English with an accuracy of 94.7%. For Swedish, which has a more complex morphology with more inflectional patterns for nouns and verbs, the accuracy is 89.3%.
To appear at Subword & Character Level Models in NLP (SCLeM) workshop at EMNLP 2017 in Copenhagen, Denmark, September 7.
Olof Mogren, Richard Johansson
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We propose an approach for named entity recognition in medical data, using a character-based deep bidirectional recurrent neural network. Such models can learn features and patterns based on the character sequence, and are not limited to a fixed vocabulary. This makes them very well suited for the NER task in the medical domain. Our experimental evaluation shows promising results, with a 60% improvement in F 1 score over the baseline, and our system generalizes well between different datasets.
To appear in Fifth workshop on building and evaluating resources for biomedical text mining (BioTxtM 2016) at COLING 2016 in Osaka, December 12.
Simon Almgren, Sean Pavlov, Olof Mogren
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Generative adversarial networks have been proposed as a way of efficiently training deep generative neural networks. We propose a generative adversarial model that works on continuous sequential data, and apply it by training it on a collection of classical music. We conclude that it generates music that sounds better and better as the model is trained, report statistics on generated music, and let the reader judge the quality by downloading the generated songs.
Constructive Machine Learning Workshop (CML) at NIPS 2016 in Barcelona, Spain, December 10.
Olof Mogren
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In this work, we present a discussion forum assistant based on deep recurrent neural networks (RNNs). The assistant is trained to perform three different tasks when faced with a question from a user. Firstly, to recommend related posts. Secondly, to recommend other users that might be able to help. Thirdly, it recommends other channels in the forum where people may discuss related topics. Our recurrent forum assistant is evaluated experimentally by prediction accuracy for the end--to--end trainable parts, as well as by performing an end-user study. We conclude that the model generalizes well, and is helpful for the users.
Representation learning for NLP, RepL4NLP at ACL 2016 in Berlin, August 11.
Jacob Hagstedt P Suorra, Olof Mogren
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Many existing methods for extracting summaries rely on comparing the similarity of two sentences in some way. In this paper, we present new ways of measuring this similarity, based on sentiment analysis and continuous vector space representations, and show that combining these together with similarity measures from existing methods, helps to create better summaries. The finding is demonstrated with MULTSUM, a novel summarization method that uses ideas from kernel methods to combine sentence similarity measures. Submodular optimization is then used to produce summaries that take several different similarity measures into account. Our method improves over the state-of-the-art on standard benchmark datasets; it is also fast and scale to large document collections, and the results are statistically significant.
RANLP 2015, Hissar, Bulgaria, September 6th-11th
Olof Mogren, Mikael Kågebäck, Devdatt Dubhashi
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A workshop paper showing preliminary results on multi-document summarization with continuous vector space models for sentence representation. The experiments were performed on opinionated online user reviews.
2nd Workshop on Continuous Vector Space Models and their Compositionality CVSC 2014, Gothenburg Sweden
Mikael Kågebäck, Olof Mogren, Nina Tahmasebi, Devdatt Dubhashi
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Continuing in the steps of Jon Kleinberg's and others celebrated work on decentralized search in small-world networks, we conduct an experimental analysis of a dynamic algorithm that produces small-world networks. We find that the algorithm adapts robustly to a wide variety of situations in realistic geographic networks with synthetic test data and with real world data, even when vertices are uneven and non-homogeneously distributed.
European Conference on Complex Systems 2009
Olof Mogren, Oskar Sandberg, Vilhelm Verendel, Devdatt Dubhashi
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This paper presents an approach to summarize online user-reviews based on finding bicliques in the bipartite word-document graph.
Proceedings of SOFSEM 2016, LNCS 9587, pp 569-579.
Azam Sheikh Muhammad, Peter Damaschke, Olof Mogren
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A white paper outlining some ideas and challenges within the field of culturomics.
International Journal on Digital Libraries, February 2015
Nina Tahmasebi, Lars Borin, Gabriele Capannini, Devdatt Dubhashi, Peter Exner, Markus Forsberg, Gerhard Gossen, Fredrik D. Johansson, Richard Johansson, Mikael Kågebäck, Olof Mogren, Pierre Nugues, Thomas Risse
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Inspired by the word-co-occurrence graph from Wikipedia documents, this paper presents an FPT approach to cluster the words.
Journal of Graph Algorithms and Applications 18 (2014)
Peter Damaschke, Olof Mogren
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